论文标题
固定点迭代用于基于图像反卷积的基于基于的PSF估计
Fixed Point Iterations for SURE-based PSF Estimation for Image Deconvolution
论文作者
论文摘要
Stein的无偏风险估计器（确定）已被证明是确定许多应用程序最佳参数的有效指标。本文的主题侧重于确定盲目卷积的参数。这些参数包括定义点扩展函数（PSF）的形状的参数以及反卷积公式中的正则化参数。在此上下文中，最佳参数通常是通过在可行的参数空间上进行搜索来确定的。当涉及多个参数时，由于维数的诅咒，此参数搜索的昂贵。在这项工作中，提出了新的固定点迭代来优化这些参数，从而可以快速估计相对较大的参数。我们证明，通过对优化参数进行一些温和的调整，这些固定点方法通常会在相对较少的迭代中收敛到理想的PSF参数，例如50-100，每次迭代都需要非常低的计算成本。
Stein's unbiased risk estimator (SURE) has been shown to be an effective metric for determining optimal parameters for many applications. The topic of this article is focused on the use of SURE for determining parameters for blind deconvolution. The parameters include those that define the shape of the point spread function (PSF), as well as regularization parameters in the deconvolution formulas. Within this context, the optimal parameters are typically determined via a brute for search over the feasible parameter space. When multiple parameters are involved, this parameter search is prohibitively costly due to the curse of dimensionality. In this work, novel fixed point iterations are proposed for optimizing these parameters, which allows for rapid estimation of a relatively large number of parameters. We demonstrate that with some mild tuning of the optimization parameters, these fixed point methods typically converge to the ideal PSF parameters in relatively few iterations, e.g. 50-100, with each iteration requiring very low computational cost.
